A novel approach to orientation estimation using inertial cues and visual feature locality constraint

This paper presents an orientation estimation methods using inertial cues (IMU) and visual feature constraint. Our proposed approach combines both of these two modalities in an original way. Two feature-point correspondences between consecutive frames are firstly selected that not merely meet the requirement of descriptor similarity constraint but the locality constraint. Secondly, these two selected correspondences together with inertial quaternions are jointly employed to derive the initial body pose. Thirdly, a coarse-to-fine procedure proceeds in removing visual false matches and in estimating body poses iteratively using the Posteriori Bayes Rule and Expectation Maximization. Eventually, the optimal orientation is estimated via the iteratively selected visual inliers. Experimental results validate that our proposed strategy is effective and accurate in orientation estimate.